Lesson video

Hello and welcome to this lesson from the unit: Reproduction in Plants.

The title of today's lesson is Factors Affecting Seed Germination: Data Analysis.

Now this lesson does follow on from a practical investigation so you may have done that with example data or you may have carried out the investigation yourself.

It doesn't matter if you haven't because we will definitely be showing you data which you can use in the tasks that follow.

My name's Mrs. Barnard, and I'm going to be taking you through today's lesson.

And by the end of today's lesson you should be able to describe and explain the results of an investigation into factors affecting seed germination.

So we've got some key terms for today's lesson, and our key terms today are table, categoric and numerical, conclusion, and trend.

Now I will put up the definitions so you can pause the video to write them down if you'd like to, or you might like to do that as we go through the lesson.

So our lesson today is in two parts.

The first part is displaying results and we're gonna be focusing on tables in today's lesson.

And the second part of our lesson is being able to interpret the results, the findings particularly of our seed germination investigation.

So let's start with the first part of today's lesson, which is displaying results.

So a scientific investigation into the factors that affect seed germination has been carried out.

Now, as I said in the title slide, you may have carried out an investigation yourself, but otherwise you can use the sample data that we are gonna share with you.

So in the investigation, a factor that might affect seed germination was selected and that is called your independent variable.

So the independent variable is the factor that you change or you choose to investigate and see what impact it has on your dependent variable.

So that dependent variable is the process that you are interested in investigating.

So in this example, we had cress seeds and we were looking at their germination.

And in this example investigation we looked at whether they had light available or no light.

And the apparatus that was set up was a Petri dish with cotton wool, and then we put cress seeds on it and we made sure that that cotton wool was soaked with water.

And because we were looking in this investigation at whether there was light or no light, one of the pieces of equipment was also a box with a lid that could close.

So as I already said, in this example investigation, the independent variable was light or no light.

And you can see from these two pictures where I placed these Petri dishes.

So one of them was placed on the windowsill in the full light, so straight on the windowsill in a nice sunny spot, and the other one was placed inside a box.

But I also put the box on the windowsill next to the Petri dish.

So in this investigation there isn't any numerical values for light.

So I didn't measure different types of light intensity to see how strong the light was.

It was just light or no light available.

So therefore our independent variable is categoric.

So it has got a label, light or no light.

So in order for us to be able to compare the effects of light and no light on seed germination, we can only change that one variable, our independent variable, so whether it has light or no light.

All the other variables which are factors that could affect seed germination had to be kept the same.

And those are called our control variables.

So if you'd like to just take a moment to think what the control variables for this investigation would be, they could have been the same for your investigation that you've carried out if you've got the opportunity to do practical work.

So maybe talk to the person next to you, pause the video and come up with what the control variables for this investigation.

Okay, I hope you got the opportunity to get a few, maybe three or four would be brilliant.

So let's have a look at what I think the control variables for this investigation are.

Now, if you have a look at the little image here, sometimes that gives you a little bit of a clue, because the things in the image in the setup are of the practical, are things that you could have to keep the same.

So in this case, the type of seed, 'cause other seeds may germinate differently to cress seeds.

So we need to make sure that we're comparing cress seeds with cress seeds.

The number of seeds, because how many are there may have an effect on how many germinate and also the distance that those seeds are spaced apart, that might also affect whether they germinate or not.

And then the mass of the cotton wool could have an effect and the volume of water that was placed in the Petri dish between the one that had light and the one that didn't have light, that could also have an effect.

And finally, the temperature could have effect.

Now, sometimes it's not always possible to keep the temperature the same unless you are using something that's thermostatically controlled.

So in this investigation I tried to keep the temperature as close as possible by putting them next to each other.

However, I didn't actually control the temperature.

I didn't put them in somewhere that was thermostatically controlled.

So time for a quick check.

So which type of variable in this investigation was the availability of light? Was it the dependent variable, the independent variable, or the control variable? Okay, so in this case, the availability of light was the independent variable.

It was a thing that I chose to investigate, light or no light.

Another check now.

So the independent variable always has different numerical values to test.

Now do you think that statement is true or false? Okay, so that statement is false.

So which of these two statements do you think best justifies the fact that that initial statement was false? Okay, the correct answer is the first one, the independent variable is the variable that is changed.

It can have categoric or numerical values.

So in the case of the investigation we just looked at, it had categoric values.

So if you got those right, then well done.

So let's move on.

So in a scientific investigation, measurements of the dependent variable are made and that shows the impact of changing the independent variable.

So in this example investigation, the seeds were left for seven days and then the number of seeds that had germinated were counted.

So that became my dependent variable.

So my dependent variable was the number of seeds that had germinated after seven days.

And that's what a germinating cress seed looks like.

You can see that the seed sort of splits open and a little sprout comes out.

So that's what I counted as a germinating seed.

So we're gonna display that information in a table.

Now the reason we display information in a table is 'cause it makes it much more easy to read.

It makes our results clear.

So in our table, my independent variable was my light availability, light or no light.

So that goes in the first column.

So the independent variable always goes in the first column of your table.

My dependent variable was the number of seeds that had germinated and that goes in my second column.

So that is a format that is always used by scientists.

You can see also that there is a full description in the heading.

So in heading number one, in the column it says light availability.

And in heading number two, it's the number of cress seeds germinated in seven days.

So it doesn't just say cress seeds germinated or germination, it's quite specific about what it's actually measuring.

So let's have a little look at the results of my investigation.

So after seven days, you can hopefully see that the seeds have germinated slightly differently in those two dishes.

And I've counted the number of seeds that have germinated.

And in the light one you can see that there are 10 and there they are.

And in the dark one you can see that there are five.

So those are the results of my investigation.

So now we need to look at putting that into a table because a table makes it easier for us to spot trends in our data.

So here is our table.

And as scientists use the same rules to present data, it makes it easier for others to understand.

So those rules are what we just explained on the previous slides, which is that our independent variable goes in the first column and our dependent variable goes in the second.

So these are the results of the example experiment that I just showed you.

So light and no light, and then we had 10 germinated after seven days in the light and five germinated after seven days in the dark.

So let's have a little look at another example.

So the title for this example investigation is the effect of water on the germination of cress seeds.

So in this example title, what do you think the independent variable is? It's different from the one before.

So talk to the person next to you perhaps and decide what the independent variable is.

Okay, so the independent variable is water, and it could be set up with categoric variables, as in water and no water like we did for light.

Or alternatively, it could be set up with numerical values where we would set up multiple Petri dishes, all exactly the same amount of cress seeds and cotton wool and spaced apart in the same way and temperature but we would put in different volumes of water.

So that investigation setup would look like this.

And you can see all the dishes are set up in exactly the same way.

The only thing that is different is the volume of water that is going to be placed into each Petri dish.

If our independent variable has numerical values, then our table looks slightly different.

We still have in the first column our independent variable, which is the volume of water.

And we still have in our second column the dependent variable, which is the number of seeds that germinated after seven days.

The difference is that in this first column, instead of our values being labels, so categoric, instead they are numerical.

So we've got the different volumes, and because we've got numerical values, we must have a unit also in the heading of the table.

Now it's important that this unit is only ever in the heading of the table and not in the body of the table.

This means that our tables are much easier to read and much tidier, and this is a rule that all scientists follow.

So time for a quick check.

So I've drawn a table here, but it's got two mistakes.

So I would like you to spot those two mistakes.

Okay? So pause the video while you do that and we'll see if you've got them right.

Okay, let's see how you got on then.

So first of all, the independent variable is in the wrong column.

So our independent variable was temperature, so it should have been in the first column.

And also temperature has numerical values, so it should have a unit.

So those are the two things that we're missing from the table.

So if you got those right, then well done.

And now it is time for us to move on to a practise task.

So what you are gonna do in the practise task is you are going to draw a table of results for your investigation, and that should include an independent variable in your first column, a dependent variable in your second column.

It should include descriptive heading titles for each column and units if you had numerical data.

Now, if you didn't carry out an investigation, you can use the example data on this next slide.

I'm not gonna give you any more information on that because I want you to use the information that's on this slide to draw your table, but you must draw it correctly.

So when you draw your tables, whether it's for your investigation or this example data, make sure you use a pencil and a ruler.

Try to use your full width of your paper if you can, that makes your table nice and big and easy to read and keeps it nice and tidy.

Okay? So come back when you are ready and we'll go through and we'll see if you have done it correctly.

Okay, let's see how you got on then with that example taste.

So this is an example table.

So this would've been based on that data you just saw, but it should have followed the same rules if it's your own table.

So you should have in that first column, temperature, and you should have the units at the top, degrees C, you should have the temperatures down the side, those numerical values, 0, 20, 40, 60 in that first column.

And then in the second column, the dependent variable, you should have a full descriptive title at the top, which was the number of cress seeds germinated in seven days.

And then you should have your values that match down there in your dependent column.

So if you got those right, or if your table that you drew for your investigation follows those rules correctly, then well done.

So it's time for us to move on to the second part of our lesson, and that is interpreting results.

So a conclusion is what we need to come to next.

So you've got your results table now from the previous task.

So now we're gonna talk through some examples of how we conclude on that data and then you are gonna have a go at doing that yourself.

So a conclusion is a summary of what has been found out by the end of an investigation.

So in my investigation here, we've got my table and some pictures to remind you what I did.

So what do you think the conclusion of this investigation would be looking at that data? So you might say, oh, well there's more light, and where there was more light, more seeds germinated.

However, it's really important that when you have a conclusion, you describe the relationship between the independent and dependent variable.

So when you write your conclusion, looking at the headings of the table is a really useful way of making sure you've got the correct detail in your conclusion.

So the correct conclusion for this table would be where light was available, more cress seeds germinated after seven days.

And you can see that I used the heading titles in describing that relationship there.

A conclusion can even go a little bit further because we can reference the numerical relationship between the data that we found in the dependent variable, if there is some present.

If there isn't, you may reference the fact that there is no numerical relationship.

So in this example here, we could say that double the number of cress seeds germinated after seven days when light was available compared to no light.

So a trend is a pattern displayed in data, and a trend can be concluded from example data.

So we've got numerical data here so we've got temperature going from 0 to 60, and then we've got different values for our dependent variable, the number of seeds that germinated.

So for example, as the temperature increased from 0 to 20 it's always useful to start with your independent variable, the number of cress seeds germinated increased.

So we can see they went from 0 to 10.

The number of cress germinated then decreases down to 0 when we go from 20 to 60 degrees C.

So we're looking at that pattern.

Now, if it's not straight, like one goes up and the other one goes up, that's all right, you can comment on it in pattern.

So comment on the starting temperatures and then the ending temperatures and what effect it has on the dependent variable.

So let's do a true or false now.

So a conclusion describes how an investigation is carried out.

Now once you've decided whether that statement is true or false, I'd like you to choose the statement below that you think best justifies that answer.

So just pause the video while you do this and then we'll come back and see how you've got on.

Okay, so the statement was that a conclusion describes how an investigation has been carried out.

Now that is false, and the reason that it's false is because a conclusion actually summarises the findings of a scientific investigation.

Remember the method is what describes how an investigation is carried out.

So a conclusion, as well as looking for trends in the data and commenting on the relationship between the independent and the dependent variable also has to have a go at explaining that relationship using scientific knowledge.

So this is where you have to bring in your knowledge of plants and how they grow in order to be able to come up with an explanation of why those dependent and independent variables reacted with each other in the way that they did.

Why that independent variable had the effect on the dependent variable that it did.

So this is my go at doing an explanation based on the findings from this investigation.

So a seed may germinate in light conditions as light is needed for the growth of a plant.

Plants use light energy to carry out photosynthesis in order to make food from carbon dioxide and water.

Therefore, a seed germinating in light will help the sprout and the seedling to survive and grow.

So what I've done there in that conclusion is I've used my knowledge of light and plants, and I know that plants use light for photosynthesis.

And then I've gone on to think about, well, what do they use it for? And they use it to make food.

And then I've gone on to think, well, why would that be useful for germination? So I'm just taking my knowledge of what I know about plants and light and then using it to form an explanation for the results of this investigation into germination.

So let's look at a different example.

So in this example, the investigation was the effect of water on germinating seeds.

So to have a go at the explanation again for this one, we need to have a think about what do we know about water and what do we know about plants? And we know that plants need water for photosynthesis, so therefore we can then take that information further to say why that might have an impact on germination.

So here is an example of an explanation here, a seed needs water to germinate.

If water is limited, then fewer seeds will germinate.

As the volume of water available increased, the number of seeds germinating increased.

This is because seeds will need water to make food as they form sprouts and seedlings.

Now, in all conclusions, you can put in as much scientific and detail as you can.

Try to include as many scientific keywords as you know and processes, and that will make sure that your conclusion is really full.

So let's time for a quick check.

So choose the statements that describe what a conclusion should include.

So you've got four there.

So pause the video and then we'll come back and we'll see how you got on.

So it should include a relationship between the variables.

It should include comment on the numerical trends, and it should include an explanation of the results using scientific knowledge.

In the conclusion we wouldn't talk about improvements for the investigation.

You would do that at some point in the investigation, but not in the conclusion.

So time for a practise task now.

So write a conclusion for your investigation.

So the one that you carried out, and if not, you can use the example data.

And in your conclusion, you should include a description of the relationship between the variables, a description of the numerical trends or absence of, and an explanation of results using scientific knowledge.

So you can use the table that you drew in task A in order to be able to complete this investigation.

I have put a copy of it here so that you can see it just in case you don't have access to that.

So it'll take you a little bit of time to write this conclusion.

So if you just pause the video and then we'll come back and we'll see how you got on after.

Okay, how did you get on with that task? Hopefully not too tricky.

So again, this is example data.

You may have used your own independent variable to comment on, but for this particular example, at 0 degrees C, no cress seeds germinated.

At 20 degrees C, 10 germinated, which was the largest number.

After 20, the number germinated decreased to five at 40, and then zero at 60.

So there we go.

That's our description of our data and our trends.

So now let's move on to our explanation.

So a seed may germinate as temperature increases as reactions such as respiration and cell division in the plant can take place more quickly.

Plants make food and grow quicker in high temperatures up to a point.

After this point, if the temperature is too high, a sprout or a seedling may quickly die.

So it's important that in your conclusion, that you have included your knowledge of plants and how they survive in order to justify any patterns that you've found in your data.

So if you've done that, then well done.

So now that brings us to the end of our lesson and our summary for today's lesson, which is Factors Affecting Seed Germination: Data Analysis, are findings can be displayed in a table with the independent variable in the first column and the dependent variable in the second.

So in our example, the number of seeds germinating was in the second column.

The independent variable can be categoric, which you remember is labels or numeric numbers.

The table heading should have full descriptions and units if there are units.

A conclusion is a description and an explanation of findings.

So for example, how did the independent variable affect seed germination? A conclusion requires results to be interpreted by identifying patterns or trends.

And a conclusion also offers an explanation for the findings using scientific knowledge and understanding.

So for example, why did changing the independent variable affect seed germination? So well done for your work on today's lesson, and we'll see you soon.